Software Engineering: Curriculum

Here you'll find detailed information on current courses of the Master's degree program Software Engineering. Please note that due to ongoing updates not all courses of the program might be fully displayed.

1. Semester

Name ECTS
SWS
Module 1.1 Software Development (MOD11)
German / kMod
6.00
-
Advanced Software Testing (AST)
German / ILV, FL
3.00
2.00

Course description

Software testing for advanced students with many practical exercises. The focus is on test case creation and test coverage for wither black- and whitebox testing. In addition test quality ist covered by residual error rate measurement and testing maturity.

Methodology

The learning outcomes are step by step through practical exercises developed. For each topic is a brief introduction, then the self-study-phase at home, and then one learning bloc together in the classroom.

Learning outcomes

After passing this course successfully students are able to ...

  • derive test cases professionally and methodically and assess their quality.
  • assess the testing maturity of an organization and estimate the number of remaining defects.

Course contents

  • Black Box Testing (how to derive high-quality test cases from the requirements)
  • White Box Testing (how to derive high quality test cases from the requirements plus the code, how to measure their quality objectively, how to measure the number of remaining defects objectively)
  • Testing Maturity (how to improve and measure the testing maturity of a testing organization)

Prerequisites

Basic knowledge in programming Basic knowledge in software testing

Literature

  • Self-study material will be provided b the teachers.

Assessment methods

  • Course immanent assessment method
Software Development (SWE)
German / ILV, FL
3.00
2.00
Module 1.2 Multimedia (MOD12)
German / kMod
6.00
-
Computer Graphics and Animation (CGA)
German / ILV
3.00
2.00

Course description

Introduction into methods of three-dimensional computer graphics and animation with practical exercises.

Learning outcomes

After passing this course successfully students are able to ...

  • develop 3D models using Blender3D.
  • furnish these models with materials and textures to render photorealistic
  • employ keyframing and skeletons to develop animations.
  • design and execute a complete CGI-animation project

Course contents

  • Course modules:
  • Modeling
  • Texturing
  • Lighting
  • Path Tracing
  • Global Illumination
  • Computer Animation

Prerequisites

English reading skills

Literature

  • Alan Watt: 3D Computer Graphics Blender Tutorials & Help

Assessment methods

  • Homework exercises
  • Written exam at end of semester
Image Processing Methods (MBV)
German / ILV
3.00
2.00

Course description

Introduction to image processing. Starting with image acquisition typical steps of an image processing pipeline are presented. The methods selected refer to three different image types which are intentsity images, range images and image sequences. Focus is given on real world applications.

Methodology

Lecture Solving practical exercises in Matlab Preparing presentations and reports on current hot topics in image processing.

Learning outcomes

After passing this course successfully students are able to ...

  • implement pixel-based and neighborhood operation-based image processing filters and effects in MATLAB
  • perform color model conversion for digital images in MATLAB.
  • filter, enhance, segment and analyze images using standard image processing methods in MATLAB and evaluate their performance.

Course contents

  • MATLAB/Octave,image acquisition. point operators, filters, 3D Vision and Image sequence analysis.

Prerequisites

imperative programming

Assessment methods

  • Course immanent assessment method and end exam

Anmerkungen

-

Module 1.3 Usability (MOD1.3)
German / iMod
6.00
-
User Centered Design (UCD)
German / ILV, FL
3.00
2.00

Course description

There are numerous software systems on the market, but many of them cause problems for the user – in the professional as well as the private domains. This costs time, money and damages the company’s image, sometimes it even causes severe safety risks. But how can we develop systems which serve the requirements and fulfil the expectations of the real users? The user centered design approach is taught, which can serve this purpose.

Methodology

This course focuses on directly applicable theoretical basics as well as numerous practical exercises and examples

Learning outcomes

After passing this course successfully students are able to ...

  • explain the necessity and advantages of a user centred design process and apply them to a concrete project
  • explain the user centred design process itself in details, plan development phases accordingly and apply them to a concrete project
  • apply a selection of state of the art methods in concrete projects

Course contents

  • Usability Engineering und UX processes, methods and their application, problems and risks
  • Cognitive and social psychology basics of UX

Prerequisites

Basic knowledge of usability enginering and UX phases are assumed A reader will be provided asap, with which students can check and complement their knowledge

Literature

  • tbd

Assessment methods

  • The blended learning activities will be continuously checked Final written exam
User Experience Evaluation (UEEV)
German / ILV, FL
3.00
2.00

Course description

This course teaches evaluation methods and challenges regarding usability and user experience measurement. Subjective experiences can be quantified and objectively measured using metrics and statistical methods.

Learning outcomes

After passing this course successfully students are able to ...

  • apply statistical methods to correctly compare different samples
  • apply these methods in a project environment
  • name various UX metrics as well as their categories, collect metrics, analyse and interpret them
  • analyse results (e.g. significance) and present them appropriately

Course contents

  • UX metrics
  • suitable statistical methods
  • data visualization
  • reproducibility of tests

Prerequisites

Basics of user centered designs and software usability

Literature

  • Bortz, Jürgen / Lienert, Gustav A. (2003) Kurzgefasste Statistik für die klinische Forschung : Leitfaden für die verteilungsfreie Analyse kleiner Stichproben, Springer, ISBN-13: 978-3540757375 Sauro, Jeff. (2012) Quantifying the User Experience: Practical Statistics for User Research, Morgan Kaufmann, ISBN-13: 978-0123849687 Tullis, Thomas / Albert, William. (2008) Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics, Morgan Kaufmann, ISBN-13: 978-0123735584 Publications and papers of respective journalsSlides

Assessment methods

  • Quizzes on the distance learning material, exercises in small groups as well as on their own, final written exam
Module 1.4 Language and Design Paradigmes (MOD14)
German / kMod
6.00
-
Advanced Modeling (AMD)
German / ILV, FL
3.00
2.00

Course description

- Well-grounded UML-knowledge - Special focus on the diagramtypes class-diagram, usecase-diagram, activity-diagram and sequence-diagram - Formal structure of the modeling language UML - UML meta model

Learning outcomes

After passing this course successfully students are able to ...

  • develop UML models showing both statical and dynamical features of IT-systems.
  • select suitable UML diagram types for modelingIT systems in practice.
  • explain the formal attributes of the modeling language UML.
  • asses the quality of UML models

Course contents

  • Formal language UML Class-diagram Usecase-diagram Activity-diagram Sequence-diagram

Prerequisites

Basic knowledge of object oriented programming

Literature

  • Weilkiens, Tim / Oestereich, Bernd: „UML 2 - Zertifizierung: Fundamental, Intermediate und Advanced"

Assessment methods

  • Course immanent assessment method
Functional Programming (FPR)
English / ILV, FL
1.50
1.00

Course description

Aspects of functional programming (lambda expressions, higher-order functions,...) have recently been added to several mainstream programming languages (C++, Java, Python,...). This course shows how to use functional concepts to create elegant, concise and easily debuggable code. Furthermore, foundations such as the lambda calculus and computational side effects are introduced.

Learning outcomes

After passing this course successfully students are able to ...

  • implement generic functional algorithms (map, scan,...) and use them for practical applications (e.g., sorting algorithms).
  • explain the concept of side effects and its implications for practical programs.
  • explain the confluence property of reduction systems and their effects on functional programming languages.
  • reduce expressions in the lambda calculus.

Course contents

  • lambda expressions
  • partial function application
  • higher-order functions
  • lambda calculus
  • computational side effects

Prerequisites

Basic programming skills in C++ or Java

Assessment methods

  • practical assignmentsfinal exam
Requirements Engineering (RME)
German / ILV, FL
1.50
1.00

Course description

The course covers the basics of methodical Requirements Engineering.

Learning outcomes

After passing this course successfully students are able to ...

  • apply requirements engineering methodically.
  • specify the structure of an requirements document.
  • define a requirements engineering process

Course contents

  • quality criteria of software requirements
  • software requirements from the process perspective

Prerequisites

Software Engineering basics

Literature

  • Pilone, Dan: "UML 2.0 - kurz & gut" Pohl, Klaus / Rupp, Chris: "Basiswissen Requirements Engineering" Robertson, Suzanne / Robertson, James(2006): „Mastering the Requirements Process”

Assessment methods

  • Course immanent assessment method
Module 1.5 IT and Management (MOD1.5)
German / kMod
6.00
-
Advanced Project Management 1 (APM1)
German / ILV
1.50
1.00

Course description

Planning of projects

Learning outcomes

After passing this course successfully students are able to ...

  • handle project and project problem with different methods (agile and classic)

Course contents

  • Planning of a project according to PMI / IPMA
  • Planning of a project with agile methods

Prerequisites

Detailed project management know how recommended - Experience in solving issues in projects

Literature

  • Projektmanagement nach PMI und IPMA
  • Portny, Stanley E., 2013, Project Management For Dummies, 4th edition, Hoboken
  • Portny, Stanley E., 2013, Projektmanagement für Dummies, 3. Auflage, Hoboken Literatur zu agilem Projektmanagement
  • Caroll, John, 2012, Agile Project Management In Easy Steps, Warwickshire
  • Layton, Mark, 2012, Agile Project Management for Dummies, Hoboken
  • Oesterreich, Bernd und Weiss, Christian, 2008, APM - Agiles Projektmanagement: Erfolgreiches Timeboxing für IT-Projekte, Heidelberg

Assessment methods

  • Continuous evaluation Evaluation of homework and presentations during presence
Information Technology and Humans (IUM)
German / ILV
1.50
1.00

Course description

What are the consequences of computer science for the individual and for humanity as a whole? A critical look at benefits and dangers.

Methodology

Seminar Flipped Classroom

Course contents

  • see german version

Prerequisites

Bachelor´s degree in computer science

Assessment methods

  • Continuous assessment

Anmerkungen

none

Leading distributed, multicultural and international teams (FMT)
German / ILV, FL
3.00
2.00

Course description

The course imparts the students theoretical knowledge of leading intercultural, dispersed and international (IDI-) teams and prepares them to implement it in a vocational context. The personal reflection, the work on case studies and the practise of opportunities of behaviour take center stage.

Learning outcomes

After passing this course successfully students are able to ...

  • analyse problems, chances and dynamics in IDI-teams (e.g. on the basis of cultural dimensions and identities) and to reflect the own behaviour.
  • outline the role of leadership in the different stages of team development (e.g. by Tuckman) particulary in IDI-teams and derive relevant leading actions.
  • explain leadership strategies in IDI-teams (e.g. functions and instruments) and develop them by means of simple cases.

Course contents

  • Multi-, inter- and transculturality
  • Cultural aspects (e.g. cultural dimensions by Hofstede, cultural identity)
  • Factors in international personnel management
  • Characteristics of dispersed teams
  • Leadership styles and tools of project teams
  • Criterias and competences for successful leadership of IDI-teams

Prerequisites

none

Literature

  • Cronenbroeck, Wolfgang (2008): Projektmanagement, Verlag Cornelsen, Berlin
  • Kellner, Hedwig (2000): Projekte konfliktfrei führen. Wie Sie ein erfolgreiches Team aufbauen, Hanser Wirtschaft
  • Majer Christian/Stabauer Luis (2010): Social competence im Projektmanagement - Projektteams führen, entwickeln, motivieren, Goldegg-Verlag, Wien
  • weitere Literatur zu interkulturellen, verteilten und internationalen Teams

Assessment methods

  • Course immanent assessment method and exame (grade)

Anmerkungen

none

2. Semester

Name ECTS
SWS
Modul 2.5 IT and Management 2 (MOD2.5)
German / kMod
6.00
-
Advanced IT Project Management 2 (APM2)
German / ILV
1.50
1.00

Course description

This course is focusing on advanced topics in project management. It is based on PMI standard, concentrating on the process groups ‘Execution’, ‘Monitoring and Controlling’ and ‘Closing’. Special topics of this course are international and intercultural aspects in virtual and dispersed teams.

Methodology

Lecture, Practice, Self-Study, Presentations, Case studies

Learning outcomes

After passing this course successfully students are able to ...

  • … name relevant cultural dimensions in international IT projects;
  • … assess international projects with reference to its risks;
  • … describe the different consequences when working with virtual and dispersed teams;
  • … define and work with adequate controlling tools in projects;

Course contents

  • Advanced IT project management with focus on internationality, interculturality and virtuality:
  • Diversity and complexity in international IT projects
  • Controlling-Tools in projects (Project Score Card)
  • Risk management in international IT projects
  • Monitoring and reviews in international IT projects

Prerequisites

IT project management 1 (1st semester)

Literature

  • PMBOK (2017) - A Guide to the Project Management Body of Knowledge (PMBOK® Guide) - 6th Edition
  • Köster, Kathrin (2009): International Project Management - London: Sage.

Assessment methods

  • Analysis of chosen countries and Diversity-Complexity-Assessment (20 points)
  • Development of Project Score Card (30 points)
  • GAP analysis and proposal for realization (20 points)
  • 2-3 pages reflection paper on additional topics (30 points)
Communication for IT-Specialists (AKITS)
German / ILV
1.50
1.00

Course description

Human communication in software projects

Methodology

Seminar Distant learning

Learning outcomes

After passing this course successfully students are able to ...

  • develop and to hold an elevator pitch
  • use a simple communication scheme to deal with resistances
  • explain consiousnes and unconsiusness aspects of human communication

Course contents

  • Communication exercises
  • Elevator Pitch
  • Resistances
  • Emotional Intelligence
  • Body language
  • NLP meta-model language patterns

Prerequisites

- Thorough knowledge of the software development cycle - Basics of human communications

Literature

  • Karsten Bredemeier. Schwarze Rhetorik - Macht und Magie der Sprache.Goldmann 2002
  • Samy Molcho. Körpersprache des Erfolges. Ariston 2005
  • A Edmüller, T Wilhelm. Manipulationstechniken erkennen und abwehren. Haufe 2005
  • A Schwarz, Ronald Schweppe. Praxisbuch NLP. Südwest 2000
  • Gerd Siemoneit-Barum und Robert Griesbeck. Die Kunst, mit dem Tier im Menschen umzugehen:Geheimnisse eines Dompteurs, Gräfe und Unzer Edition, 2007

Assessment methods

  • Continuous assessment
Legal Aspects of Information Technologies (RAIT)
German / ILV
1.50
1.00
Master Project Planning (MPP)
German / PRJ
1.50
1.00

Course description

This course is the first in a series of courses directly connected to the master thesis. In this course you will pick/decide on a project for the upcoming courses "Master Project" and "Master Thesis", state a first version of the research question(s)/hypothesis of the work and establish required project infrastructure (communication, reporting, legal issues, etc.). The goal is to complete the pre-project phase in order to be able to start working on the project without further delay at the beginning of the 3rd semester (or even before the summer, if desired).

Methodology

kick-off, individual coaching

Learning outcomes

After passing this course successfully students are able to ...

  • to start working on the final project without further delay at the beginning of the 3rd semester.

Course contents

  • project selestion, project infrastructure

Prerequisites

Basics Project Management

Assessment methods

  • participated with/without success

Anmerkungen

-

Modul External Lecture (MODso)
German / kMod
-
-
International Semester (AS2)
German / SO
30.00
0.00
Module 2.1 Software Quality (MOD21)
German / kMod
6.00
-
Advanced Software Quality Management (SQM)
German / ILV
3.00
2.00

Course description

In the first two units, the students get an overview of software quality management. In the two units after that, there are deep dives into software estimation quality and into the quality of visualizations of management data. Most of the case studies are taken from industrial practice. There are several crosscutting case studies used in all of the teacher’s courses to show the connections of the software engineering disciplines in more detail.

Methodology

Self study (knowledge + practical exercises) In class, there are 1) Clarifications, if needed 2) Presentations of the results achieved at home 3) Group exercises 4) Individual exercises 5) Exchange of experiences made in the industry

Learning outcomes

After passing this course successfully students are able to ...

  • 1) create high quality results
  • 2) review other person’s results
  • 3) reflect on the method used and adapt it to the concrete project
  • in all fields listed under „learning outcomes“.

Course contents

  • Writing a software quality plan
  • Analytical, constructive and reparative measures in software quality management
  • Quality measures against requirements defects, implementation defects and usage defects
  • Quality measures concerning processes, people and tools
  • Poka Yoke
  • Quality Function Deployment (QFD)
  • Failure Modes and Effects Analysis (FMEA)
  • Effort estimation methods (expert estimate, Story Points, Classic Function Points, Delphi-Method)
  • Risk estimation methods
  • Definition of metrics in software engineering (LOC, McCabe, Halstead, …)
  • Visualization of metrics in software engineering

Prerequisites

Quality management and software development on the bachelor level Professional experience in software engineering

Literature

  • Reading tips can be found in the slidedeck.

Assessment methods

  • Grading is based on ...
  • individual practice in class
  • group practice in class
  • offsite practice
Software Frameworks (SFR)
German / ILV, FL
3.00
2.00

Learning outcomes

After passing this course successfully students are able to ...

  • explain the concept of software frameworks
  • design a software framework including the abtract and real casses, using Role Based Modeling
  • using an OSGi implementation to implement an application or Framework

Course contents

  • OSGi, Role Model Based Framework Design, API Design

Prerequisites

Java

Module 2.2 Visualization (MOD2.2)
German / kMod
6.00
-
Information Visualization (INVI)
German / ILV
3.00
2.00

Course description

Data analysis and exploratory statistics using interactive visualization techniques.

Learning outcomes

After passing this course successfully students are able to ...

  • specify the inherent properties of differenttypes of data (e.g. discrete, continuous, hierarchical, temporal)
  • select and apply visualization techniques for a specific type of data
  • know properties of data visualisation aims to display
  • create linked, interactive visualizations auf a CSV dataset

Course contents

  • Types of data
  • base statistics
  • Visualization techniques and examples
  • Interactive visual analysis
  • Creation of interactive visualizations using D3.js

Prerequisites

- Programming knowledge (the exercises are based on R and Latex) - Basic knowledge of statistical terms (e.g., normal distribution, deviation, median)

Literature

  • Benjamin B. Bederson, Ben Shneiderman (2003): The Craft of Information Visualization: Readings and Reflections, Morgan Kaufmann, ISBN 1-55860-915-6
  • Hatzinger, R., Hornik, K. and Nagel, H.; R Einführung durch angewandte Statistik
  • Venables, W. and Ripley, B.; Modern Applied Statistics with S

Assessment methods

  • Exercises and written exam.
Visual Computing (VICO)
German / ILV
3.00
2.00

Course description

Lecture and exercises on selected topics of Visual Computing

Methodology

Lecture Student presentations on selected topics Solving practical tasks by the students

Learning outcomes

After passing this course successfully students are able to ...

  • explain image acquisition processes for 2D and 3D images
  • analyse image sequences with respect to time and objects
  • perform feasibility studies for visual computing tasks
  • apply deep learning to images
  • understand ubiquitous computing related to images

Course contents

  • Object Recognition
  • Visual Ubiquitous Computing
  • Deep Learning
  • Content Based Information Retrieval
  • Biometry and Facial Analysis
  • Applications

Prerequisites

Methoden der Bildverarbeitung

Literature

  • Gonzalez-Woods (2003): Digital Image Processing, Prentice Hall International, ISBN 0201180758

Assessment methods

  • LV-Immanente Leistungsbeurteilung und Abschlussprüfung
Module 2.3 Data and Design (MOD2.3)
German / kMod
6.00
-
Big and Linked Data (BLD)
German / ILV
3.00
2.00

Course description

This course discusses basic concepts in Big and Linked Data. State-of-the-art technologies in the areas of NoSQL databases, data-intensive programming and semantics are theoretically and practically elaborated.

Learning outcomes

After passing this course successfully students are able to ...

  • evaluate NoSQL database systems regarding consistency, availability, and partition tolerance and to exemplify their functionality
  • utilize advanced massive parallel programming paradigms (e.g. Apache Spark) and to evaluate regarding scalable machine learning algorithms
  • characterize concepts from LinkedData and Ontologies
  • consume, model, and produce LinkedData

Course contents

  • MapReduce and data-intensive programmingNoSQL systemsscalable machine learningLinked Data and ontologies

Prerequisites

distributed systemsdatabase systemsprgramming skills

Literature

  • Arun Murthy, Apache YARN - Moving beyond MapReduce and Batch Processing with Apache Hadoop 2, 2014Matei Zaharia, et. al., Learning Spark, O'Reilly Media, Inc., 2015Martin Köhler, et. al., #BigData in #Austria - Österreichische Potenziale und Best Practice für Big Data, 2014Helmut Berger, et. al. Conquering Data in Austria, 2014Toby Segaran, et. al., Programming the Semantic Web, 2009Christoh Bizer, et. al., Linked Data - The Story So Far, 2009.

Assessment methods

  • lab assignmentsself-assessment testswritten exam
Interaction Design (IAD)
German / ILV, FL
3.00
2.00

Course description

The Interaction Design course teaches important aspects of software engineering, the iterative user centered design process in terms of user interface development. In this his course students develop their own user interface prototypes in groups.

Methodology

Problem und Project Based Learning

Learning outcomes

After passing this course successfully students are able to ...

  • design simple user interfaces
  • explain the importance of user interface design
  • plan user interface design processes within software development processes

Course contents

  • Interaction Design und Interface Design
  • Developing multiple user interfaces independently
  • Project Based Learning

Prerequisites

Principles of computer science, principles of user centered design

Literature

  • Tidwell, Jenifer, Designing Interfaces, O'Reilly Media, 2. Auflage 2011, ISBN-13: 978-1449379704Weinschenk, Susan, 100 Things Every Designer Needs to Know About People, 1. Auflage 2011, ISBN-13: 978-0321767530Saffer, Dan, Designing for Interaction, New Riders, 2. Auflage 2009, ISBN-13: 978-0321643391Unger, Russ, Chandler, Carolyn, A Project Guide to UX Design, New Riders 1. Auflage 2009, ISBN-13: 978-0321607379Cooper, Alan, Reimann, Robert, Cronin, David, About Face; 1. Auflage 2010, ISBN-13: 978-3826658884

Assessment methods

  • Continuous assessment

Anmerkungen

Students work mainly on real world projects. The supervision is done on an individual basis in synchronous or asynchronous settings and is supported by modern communication tools. The course is partially or not displayed in the timetable and no attendance records are kept.

Module 2.4 Advanced Computing (MOD24)
German / kMod
6.00
-
High-Performance Computing (HPC)
German / ILV, FL
3.00
2.00

Course description

This course gives an introduction to parallel programming on Graphics Processing Units (GPUs) with respect to high performance. It covers the hardware archtitecture of GPUs as well as the parallel programming API OpenCL.

Learning outcomes

After passing this course successfully students are able to ...

  • implement basic OpenCL applications (e.g., image filters).
  • explain the basic architecture of a GPU and associated parallel programming models.
  • highlight differences between the OpenCL/GPU memory model and the CPU/main RAM model and explain resulting implications for highly efficient parallel programs.
  • implement the scan algorithm in OpenCL.
  • explain the application of the scan algorithm in parallel applications such as sorting or image processing

Course contents

  • Parallel programming paradigms and algorithms
  • OpenCL programming
  • GPU architecture and memory model
  • performance optimization of parallel programs

Prerequisites

C++ or Java programming skills

Literature

  • McCool, Robison, Reinders: Structured Parallel Programming. Elsevier, 2012

Assessment methods

  • Course immanent assessment method:
  • self evaluation exercises (online)
  • programming project
  • final presentation
Parallel Programming (PPR)
German / ILV
3.00
2.00

Course description

Parallel programming with multithreading

Methodology

Lecture with practical exercises and homework.

Learning outcomes

After passing this course successfully students are able to ...

  • understand and work with concurrency primitives (e.g. Monitors) in real-world scenarios
  • explain and countermeasure problems such as race conditions or deadlocks
  • analyze sequential programs for potential speedup via parallel execution as well as the parallel implementation
  • implement loops and divide-and-conquer algorithms in a parallel way such that the overall-performance increases
  • understand concepts (Threadpools, Data-parallelism and task parallelism) typically found in parallel programming frameworks such as OpenMP, CilkPlus, TPL and Java Parallel streams
  • understand and countermeasure practical performance problems such as oversubscription and false sharing

Course contents

  • Development and application of parallel programming concepts. In practial exercises those concepts will be realized in C# and C. Differences and similarities between concrete implementations (as found in CilkPlus or OpenMP) are explained and discussed.

Prerequisites

C basic knowledge, very good programming skills in at least one programming language

Literature

  • Michael McCool et al, Structured Parallel Programming: Patterns for Efficient Computation. Morgan Kaufmann, 2012
  • Tim Mattson et al, Patterns for Parallel Programming. Addison-Wesley Professional, 2004

Assessment methods

  • Course immanent assessment method

3. Semester

Name ECTS
SWS
Modul External Lecture (MODso)
German / kMod
-
-
International Semester (AS3)
German / SO
12.00
0.00
Module 3.1 Mandatory Courses (MOD31)
German / kMod
6.00
-
Module 3.1A - Elective Courses A (MOD3A)
German / kMod
3.00
-
Advanced Web Technologies (AWT)
German / ILV, FL
3.00
2.00
Augmented Reality (AMR)
German / ILV, FL
3.00
2.00

Course description

Augmented reality (AR) is the connection of real and virtual content. In this course, the technological basics and practical applications of AR are presented.

Learning outcomes

After passing this course successfully students are able to ...

  • differ between different augmented reality systems, characterize them (image-based, sensor-based) and appropriately select them for different use cases (e.g. stationary installation, mobile application, etc.)
  • analyze and evaluate different image-based tracking methods (marker, NFT, SLAM, 3D tracking) regarding their tracking performance
  • and eventually select existing AR software frameworks (e.g. Metaio SDK, vuforia SDK, etc.) in order to use them in their own projects or implement their own AR applications. In order to achieve this, students will implement a mobile AR app with predefined functions (tracker change, content change, simple animation, calculations of tracking pose, etc.)

Course contents

  • AR basics & tracking methods: marker based-, NFT-, SLAM- and 3D trackingn- Rendering, OpenGL, CG, materials, textures, transparency, 3D enginesn- GPS based AR, audio AR, AR glasses (Google Glass, Epson)n- AR SDKs and frameworksn- Interaction, animation, picking

Prerequisites

Basic knowledge of computer graphics and computer vision as well as basic mathematical knowledge are an advantage.

Assessment methods

  • Regular assessment, practical exams, final exam.
Concepts of Programming Languages (SPK)
German / ILV, FL
3.00
2.00

Course description

Concepts of Progamming languages, programming paradigms and foundations of compilers and interpreters

Methodology

Lecture part, exercises, programming project, interview

Learning outcomes

After passing this course successfully students are able to ...

  • apply a programming paradigm in programming languages
  • create simple programs in functional and logical programming languages
  • design and use regular expressions and grammars
  • implement a basic compiler using parser generators

Course contents

  • Imperative programming paradigm
  • Object oriented paradigm
  • Foundations of functional programming
  • Halting problem: Kurt Goedel and limits of programming languages
  • Logic programming languages
  • Regular expressions
  • Grammars
  • Parallel language constructs
  • Foundations of compiler construction

Prerequisites

C basic knowledge, function parameters

Literature

  • Donald Knuth. The Art of Computer Programming

Assessment methods

  • 60% exercises,10% quizzes, 30% final exam where studens must get at least half the points on the final exam.

Anmerkungen

Course immanent assessment method and end exam

Introduction to Graph Databases (GDB)
English / ILV, FL
3.00
2.00
Mental Power for IT Disciplines (MIT)
German / ILV, FL
3.00
2.00

Course description

In thus course you will learn to use the whole capacity of your brain to solve problems and to achieve any goal you wish

Methodology

Seminar and distant learning

Learning outcomes

After passing this course successfully students are able to ...

  • formulate goals you want to achieve which are suitable for your subconsious mind
  • practicing basic elements of attention meditation
  • focus the conscious mind on goals to align unconscious processes

Course contents

  • Processing of information in the human brain
  • Consciousness and unconsciousness parts of the brain
  • Gaining consciousness use of primarily unconsciousness parts of the brain
  • Using skill full meditation techniques to improvebusiness performance

Prerequisites

none

Literature

  • James Borg, "Mind Power", Pearson 2010
  • Kazuo Inamori, "A Compass to Fulfillment", Mc Graw Hill 2010
  • Heinz Hilbrecht, "Meditation und Gehirn", Schattauer, 2010
  • Richard Bandler, "Veränderung des subjektiven Erlebens", Jungfern Verlag 2007, Original: "Using your brain - for a change", Real People Press, U.S. (August 1985)
  • Henry P. Stapp, "Mindful Universe" 2nd Edt Springer 2011
  • Chade-Meng Tan "Search Inside Yourself" Optimiere dein Leben durch Achtsamkeit, Goldmann Verlag 2015

Assessment methods

  • Continuous assessment

Anmerkungen

none

Mobile Application Engineering (EPMA)
English / ILV
3.00
2.00

Course description

Introduction into app development for Android and iOS.

Learning outcomes

After passing this course successfully students are able to ...

  • After successfully completing the course, students are able to
  • develop Android and iOS Apps, using the latest Development Environment and Toolchain
  • describe the Liefcycle of Smartphone Applications and explain common concepts in the areas of Testing, Publishing, Marketing & Business Models
  • estimate the required resources for a feature implementation on Android and iOS

Course contents

  • Android and iOS app development and source control management with Git.

Prerequisites

Basic software development experience with Java / C/C++ / Objective C.

Literature

  • Joseph Anuzzi Jr, Lauren Dracay, Shane Conder (2014): Advanced Android Application Development, Addison-Wesley ProfessionalNeil Smyth (2015): iOS 8 App Development Essentials - Second Edition: Learn to Develop iOS 8 Apps using Xcode and Swift 1.2, CreateSpace Independent Publishing Platform

Assessment methods

  • Participation, development of the project, delivery dates, clean source code with comments and Git commits.
Software Measurement (SWM)
German / ILV
1.50
1.00

Course description

The 14 teaching modules are grouped in two blocks of 4 hours and two blocks of 3 hours. Of the 14 hours 7 are on site and 7 are self teaching. Each block handles another level of software measurement. In the first block requirement metrics are taught, in the second design metric, in the third code metric and in the fourth test metrics. For each block there is an exercise in software measurement which the students have to solve as a team. At the end there is a written exam in which students are required to answer a set of 40 questions based on a check list they are given prior to the exam.

Methodology

Docent lectures, remote learning with recorded lectures, exercises and case studies.

Learning outcomes

After passing this course successfully students are able to ...

  • After passing this course successfully students are able to
  • define and compare software metrics.
  • measure the size and complexity of software systems in order to estimate the costs of developing, maintaining and migrating them.
  • measure the quality of software and the amount of technical debt.
  • measure the quality of the software test and to decide when to release the software product.

Course contents

  • The first teaching unit is devoted mainly to requirement metrics. It includes the four lectures on:
  • Software Measurement – Definition and Selection, Goal/Question/Metric Method.
  • Software Quantity Measurement – Data-Points, Object-Points, Function-Points, UseCase-Points, Statements, LOCs.
  • Software Complexity Measurement – Functional Complexity. Data flow complexity, Control flow complexity, Coupling and Cohesion, Fan-in, Fan-Out
  • Requirement Metrics – Requirement Sizing, Requirement Complexity, Requirement Qualities, Completeness, Complexity, Conformity. The second teaching unit is devoted mainly to design metrics. It includes the three lectures on:
  • Qualtity Measurement – ISO-9126, Functionality, Reliability, Performance, Usability, Security, Maintainability, Portability, Interoperability.
  • Productivity Measurement – Design Diagrams, Code-Modules, Test Cases, Documentation Pages, Statements, Function-Points per person day.
  • Design Metrics – UML Elements, UML Relationships, Architectural Complexity, Architectural Quality, Model Measurement, UML, XMI. The third teaching unit is devoted to code metrics. It includes the four lectures:
  • COCOMO – Code size and complexity, Kilo Delivered Statements, Influence factors, Exponent factors, Code productivity, LOCs, KDSI
  • Code Metrics – Statement counting, data counting, Fan-in, Fan-Out, cyclomatic complexity, language complexity, Modularity, Reusability, Security.
  • Data Metrics – SQL Schemata, Tables, Keys, Attributes, Relationships, XML Schemata, Tree width and depth, Tags, References, Data-Points
  • System Evaluation – Measurement and Evaluation procedural and object-oriented systems with SoftAudit and SoftEval, Case Studies. The fourth teaching unit is devoted to test metrics. It includes the three lectures:
  • Test metric – Test objekts, Test cases, Test Prozedures, Test Coverage. statement, branch and path coverage, data coverage, Interface coverage.
  • Error Metrics – ANSI/IEEE 1044, Error Classification, Error Types, Error Severity, defect density, defect projection, remaining error probability.
  • Software Economics – Cost Drivers, Development costs, Test costs, Maintenance costs, Total Cost of Ownership, Make or Buy.

Prerequisites

Students should have UML and programming knowledge. They should be familiar with the principles of software engineering.

Literature

  • Dumke/Ebert: Software Measurement, Springer VerlagSneed/Seidl/Baumgartner: Software in Zahlen, Hanser Verlag

Assessment methods

  • 40% is the average exercise grade, 60% is the exam grade

Anmerkungen

The course is built around the four exercises – Requirements Measurement, UML-Measurement, Java/C#/PHP Measurement, Test Measurement

Test Automation (TAM)
German / ILV
1.50
1.00

Course description

This English language course takes the participants through the world of test automation from the analysis of the requirements to the evaluation of the test results. On the way through several different testing tools are handled, some from the Open Source community and some from the lecturer himself. The emphasis is on practical application. Every week a test automation exercise has to be performed and the results submitted. The course ends with a written exam.

Methodology

Lecture with interactive elements, teamwork-based exercises, online interaction, video tutorials

Learning outcomes

After passing this course successfully students are able to ...

  • After passing this course successfully students are able to
  • test complex software systems, databases, web systems and web services with the aid of automated tools.
  • generate test data.
  • define a metastructur for testcases, which fits for automated testing.
  • undertake a performancetest.

Course contents

  • Requirement-based Testing
  • Automated Requirements Analysis and Test Case Specification
  • Model-based Testing
  • Database testing
  • Test data management
  • Client/Server System Testing in Behaviour- and Acceptance-Test Driven Development
  • Web application testing with Selenium
  • Structuring automated Test Cases
  • Performance Testing
  • Web Service Testing
  • SOAP and REST Request Generation
  • SOAP and REST Result Validation
  • System interface simulation for testing purposes
  • Continuous Testing

Prerequisites

Participants should have programming knowledge (Java). They should be familiar with SQL queries and know about the basics of XML and/or JSON (basics). Wissen über Webapplikationen und HTTP ist von Vorteil.

Literature

  • Bucsics,Thomas / Seidl, Richard / Baumgartner, Manfred (2013): „Basiswissen Testautomatisierung: Konzepte, Methoden und Techniken“, dpunkt Verlag; Sneed, Harry / Baumgartner, Manfred / Seidl, Richard (2007): “Der Systemtest”, Hanser Verlag

Assessment methods

  • 100% of the grade is determined by the average exercise grade

Anmerkungen

This course requires hands on working in teams to accomplish the exercises.

Module 3.1B - Elective Courses B (MOD3B)
German / kMod
3.00
-
Advanced Design Patterns for Smartphone Applications (DSM)
English / ILV, FL
3.00
2.00

Course description

Design Patterns for the Development of Smartphone Apps for Android and iOS.

Learning outcomes

After passing this course successfully students are able to ...

  • name and describe the unique characteristics of the respective platform's programming language
  • explain advanced design patterns for smartphone applications and apply them in their apps
  • find appropriate 3rd party frameworks for feature implementations and correctly integrate them in their app architecture

Course contents

  • Android and iOS app design patterns.

Prerequisites

Basic software development experience with Java / C/C++ / Objective C.

Literature

  • Mike Rogers (2015): Swift Recipes: Problem-Solution Approach, ApressDave Smith (2015): Android Recipes: A Problem-Solution Approach for Android 5.0, Apress

Assessment methods

  • End exam
Application Lifecycle Management (ALM)
German / ILV, FL
1.50
1.00

Course description

The course enables the student to make sound decisions over the whole software life cycle. The student learns about the most widely used release and branching methods, methods for continuous software enhancement and structured defect management.

Learning outcomes

After passing this course successfully students are able to ...

  • After passing this course successfully students are able to

Prerequisites

Software Engineering

Assessment methods

  • Course immanent assessment method
Cloud Platforms and IT Security (CIS)
German / ILV, FL
3.00
2.00

Course description

Quality and security of clouds is a requirement for their successful use. With regard to quality, numerous standards are available. We study these standards with regard to clouds and assess their quality and usefulness. With regard to security, we identify and analyze typical security problems in cloud setups. Further, we discuss difference to ordinary deployments as well as countermeasures. Within projects, we apply our previously gained knowledge.

Methodology

Presentation and practical exercisees

Learning outcomes

After passing this course successfully students are able to ...

  • identify suitable standards for cloud solution.
  • define meaningful criteria for cloud solutions.
  • choose adequate cloud solutions/platforms.
  • describe and identify security issues in cloud solutions.
  • identify and implement adequate countermeasures.

Course contents

  • Introduction to standards in the context of clouds
  • Assessment of standards in the context of couds
  • Introduction to security matters and solutions in the context of clouds

Prerequisites

Basic understanding of standards programming knowledge

Literature

  • https://staraudit.org/
  • https://cloudsecurityalliance.org/

Assessment methods

  • Exercise and presentation of evaluation criteria
  • Project on assessment of cloud service
  • Exercise measurement of side channels
  • Short reports on content
Internet Vision (IVS)
German / ILV, FL
3.00
2.00

Course description

Lecture with exercises on Computer Vision, Computer Graphics and Multimedia applied to the Internet.

Learning outcomes

After passing this course successfully students are able to ...

  • After passing this course successfully students are able to• generate new content (i.e. images) from internet data• interpret images based on their content described by image features• apply combinations of deep learning and biddata applications on digital images

Course contents

  • Internet Vision Fundamentals
  • Big Data – Internet of Things
  • Deep Learning
  • Lifelogging
  • Object Recognition
  • Scene Completion and other applications

Prerequisites

Matlab and Visual Computing

Literature

  • Hays J., Alexei A. Efros (2007). Completion Using Millions of Photographs.. ACM Transactions on Graphics (SIGGRAPH 2007). vol. 26, No. 3.
  • Jing Y. and Baluja S.. (2008) :PageRank for Product Image Search, 17th International IEEE World Wide Web Conference.
  • Snavely N., Seitz S.M., Szeliski R. (2006). Photo tourism: exploring photo collections in 3D, ACM SIGGRAPH, pp 835-846.
  • Stone, Z.; Zickler, T.; Darrell, T., Toward Large-Scale Face Recognition Using Social Network Context, Proceedings of the IEEE , vol.98, no.8, pp.1408,1415, Aug. 2010

Assessment methods

  • Course immanent assessment method
  • Presentation and life demo of selected topics
  • Review of scientific reports/papers
  • Evaluation/Tests of available tools on the web
Machine Learning (MAL)
German / ILV, FL
3.00
2.00

Course description

The course offers an introduction to the methods of machine learning, with a special focus on supervised learning methods, up to current topics such as deep learning. The course covers the entire analysis process, from the data processing to the evaluation of the models.

Methodology

Lecture and working on practical examples

Learning outcomes

After passing this course successfully students are able to ...

  • To recognize and define problems in data analysis as a machine learning task
  • select appropriate methods for data processing and select a suitable learning algorithm
  • assess quality and practical viability of models and results

Course contents

  • Overview on unsupervised and supervised learning methods, as well as their fields of application
  • Overview on feature extraction methods, for multi-modal content (image, audio, text, ..)
  • Methods of Data Pre-processing: Encoding, Normalization / Standardization, Corelation Analysis, Feature / Attribute Selection
  • Functionality of popular machine learning algorithms: k-NN, Naive Bayes, Decision Trees, Random Forests, Perceptron and Neural Networks, Support Vector Machines
  • Ensemble Learning
  • Deep Learning
  • Evaluation of machine learning models: experiment setup, key figures for the assessment of performance, significance analysis, cost functions

Prerequisites

Basic knowledge in statistics, basic knowledge in one of the programming languages Java / R / Matlab / Python

Literature

  • Tom Mitchell "Machine Learning", Christopher M. Bishop "Pattern Recognition"

Assessment methods

  • continuous assessment, final exam.
Software Architecture (SWA)
German / ILV, FL
1.50
1.00

Course description

The course enables the student to make architectural decisions based on the concrete software requirements. The course conveys basic knowledge about software architectures, quality criteria for architectures and evaluation methods of architectures.

Learning outcomes

After passing this course successfully students are able to ...

  • After passing this course successfully students are able to• Design architecture metamodels• Design software architectures• Evaluate software architectures.
  • After passing this course successfully students are able to
  • Design architecture metamodels
  • Design software architectures
  • Evaluate software architectures.

Course contents

  • - Founding Terms of Architecture- Architecture Documentation and Communication- Development of Software Architectures- Architecture and Quality- Tools for Architects

Prerequisites

UML, Quality Management

Literature

  • Mahbouba Gharbi „Basiswissen Softwarearchitektur“

Assessment methods

  • Course immanent assessment method
Module 3.2 Master's Project (MOD32)
English / iMod
24.00
-
Master´s Project (MPR)
English / PRJ
21.00
14.00

Course description

The course provides space for preparatory activities for the Master Thesis carried out as a project. The results are incorporated in the Master Thesis.

Learning outcomes

After passing this course successfully students are able to ...

  • After successful completing the course, students are able to… write their master thesis in accordance to the rules of project management.

Course contents

  • Preparatory work for the Master's thesis For example:
  • Programming activities
  • Theoretical work
  • Participation in IT projects
  • Evaluation of technologies and products with scientific methods
  • Feasibility study, prototype development

Prerequisites

Courses of the first and second semester of the master software development

Literature

  • Books:For the project, relevant textbooksJournals:For the project, relevant journals

Assessment methods

  • Assessment of the master’s thesis project

Anmerkungen

The supervision is done on an individual basis in synchronous or asynchronous settings and is supported by modern communication tools. The course is not displayed in the timetable and no attendance records are kept.

Project Seminar (SMP)
German / SE
1.50
1.00

Course description

Support for the master project, especially for scientific issues. Is integral of the master´s project.

Learning outcomes

After passing this course successfully students are able to ...

  • After successful completing the course, students are able to, … write their master thesis in accordance to the rules of project management

Course contents

  • Preparatory work for the Master's thesis For example:
  • Programming activities
  • Theoretical work
  • Participation in IT projects
  • Evaluation of technologies and products with scientific methods
  • Feasibility study, prototype development

Prerequisites

Courses of the first and second semester of the master software development

Literature

  • Books:For the project, relevant textbooksJournals:For the project, relevant journals

Assessment methods

  • Assessment of the master’s thesis project

Anmerkungen

The supervision of the master’s thesis is done on an individual basis in synchronous or asynchronous settings and is supported by modern communication tools. The course is partially or not displayed in the timetable and no attendance records are kept.

Scientific Work (WA)
German / SE
1.50
1.00

Course description

How do I write a master thesis in Software Engineering?

Learning outcomes

After passing this course successfully students are able to ...

  • identify the basic types of scientific publications and differentiate between them, especially original papers, review papers, conference articles, journals and books
  • evaluate subject specific literature sources (also in English) regarding confirmability, dependability, plausibility, and transferability of insights for comparable problems or contexts and use and reference these in their own work
  • justify a research question after identifying the current state of the art with regard to scientific considerations, formulate the question comprehensibly and to define verifiable target criteria
  • plan the phases of a scientic study, conduct it precisely, document it comprehensibly, and to ensure the comprehensibility, dependability, plausibility and transferability other problems areas and contexts
  • chose and apply relevant methods for the research question and compose the structure, a proposal and the Master thesis, especially * engineering modelling, development and test methods * experimental research methods * empirical methods of data collection
  • relate research results to industry, society, the economy or the environment.
  • present own or other scientific publications comprehensibly, evaluate them and formulate suggestions for further development.

Course contents

  • Scientific quoting
  • Literature research
  • Structur of a master thesis
  • Spelling style
  • Philosophy of science

Prerequisites

Logic, writing bachelor thesis

Assessment methods

  • Course immanent assessment method

4. Semester

Name ECTS
SWS
Modul 4.1 (MOD41)
German / kMod
6.00
-
Modul 4.1A - Elective Courses A (MOD4.1A)
German / kMod
6.00
-
Advanced Design Patterns for Smartphone Applications (FDPSM)
English / ILV, FL
3.00
2.00

Course description

Design Patterns for the Development of Smartphone Apps for Android and iOS.

Learning outcomes

After passing this course successfully students are able to ...

  • name and describe the unique characteristics of the respective platform's programming language
  • explain advanced design patterns for smartphone applications and apply them in their apps
  • find appropriate 3rd party frameworks for feature implementations and correctly integrate them in their app architecture

Course contents

  • Android and iOS app design patterns.

Prerequisites

Basic software development experience with Java / C/C++ / Objective C.

Literature

  • Mike Rogers (2015): Swift Recipes: Problem-Solution Approach, ApressDave Smith (2015): Android Recipes: A Problem-Solution Approach for Android 5.0, Apress

Assessment methods

  • End exam
Augmented Reality (AMR)
English / ILV, FL
3.00
2.00

Course description

Augmented reality (AR) is the connection of real and virtual content. In this course, the technological basics and practical applications of AR are presented.

Methodology

Seminar and distant learning

Learning outcomes

After passing this course successfully students are able to ...

  • differ between different augmented reality systems, characterize them (image-based, sensor-based) and appropriately select them for different use cases (e.g. stationary installation, mobile application, etc.)
  • analyze and evaluate different image-based tracking methods (marker, NFT, SLAM, 3D tracking) regarding their tracking performance
  • and eventually select existing AR software frameworks (e.g. Metaio SDK, vuforia SDK, etc.) in order to use them in their own projects or implement their own AR applications. In order to achieve this, students will implement a mobile AR app with predefined functions (tracker change, content change, simple animation, calculations of tracking pose, etc.)

Course contents

  • AR basics & tracking methods: marker based-, NFT-, SLAM- and 3D trackingn- Rendering, OpenGL, CG, materials, textures, transparency, 3D enginesn- GPS based AR, audio AR, AR glasses (Google Glass, Epson)n- AR SDKs and frameworksn- Interaction, animation, picking

Prerequisites

Basic knowledge of computer graphics and computer vision as well as basic mathematical knowledge are an advantage.

Assessment methods

  • continuous assessment, practical exams, final exam.
Big Data Analytics (BDA)
German / ILV, FL
3.00
2.00

Course description

The course provides an introduction to methods of data mining, with a focus on unsupervised learning methods such as clustering or association rule mining and collaborative filtering, data projection methods, and anomaly/outlier detection. The LVA covers the complete data analysis process, using process models such as Fayyad's Knowledge Discovery in Databases process or the CRISP-DM (Cross-industry standard process for data mining).

Methodology

Lecture and exercises

Learning outcomes

After passing this course successfully students are able to ...

  • Extract relevant knowledge from large databases
  • Support business decisions with Data Mining
  • Analyze business problems and questions with the help of data
  • Select of suitable data mining algorithms and methods to meet a problem definition

Course contents

  • Data projection / Dimensionality reduction
  • Clustering
  • Association Rule Mining / Collaborative Filtering
  • Anomaly detection / Outlier detection
  • Privacy-preserving data analysis / secure computation

Prerequisites

Basic knowledge of statistics (important basics are repeated)

Assessment methods

  • Exercises and final exam
Docker / Swagger (DOSW)
German / ILV, FL
3.00
2.00

Course description

This course will provide an overview of the capabilities and possibilities of using container-based virtualization technologies, examining Docker as an example in detail. Additionally Swagger, a framework to create RESTful services/APIs will be explored.

Methodology

Seminar and distant learning

Learning outcomes

After passing this course successfully students are able to ...

  • understand and explain container-based virtualization
  • decide when (not) to use container-based virtualization
  • understand and explain RESTful services/APIs
  • create a RESTful service/API using Swagger

Course contents

  • overview of different virtualization technologies
  • Docker, a container-based virtualization technology
  • RESTful services/APIs
  • Swagger, a framework to create RESTful services/APIs

Prerequisites

none (basic knowledge of IT/concept of virtualization helpful)

Literature

  • https://docs.docker.com/get-started/
  • https://swagger.io/getting-started/

Assessment methods

  • Continuous assessment
Internet Vision (IVI)
English / ILV, FL
3.00
2.00

Course description

Lecture with integrated exercises on Compute Vision, Computer graphics applied to large scale images

Methodology

Thematic presentations by the lecturer & selected topics by the students Hands On and exercise to be solved by the students Extented summary of a scientific paper

Learning outcomes

After passing this course successfully students are able to ...

  • data generation from large scale images
  • content based information retrieval
  • Deep Learning - BigData - IoT

Course contents

  • Internet Vision Fundamentals
  • Big Data – Internet of Things
  • Lifelogging
  • Scene Completion and other thematic applications

Prerequisites

Fundamentals Visual Computing

Literature

  • Hays J., Alexei A. Efros (2007). Completion Using Millions of Photographs.. ACM Transactions on Graphics (SIGGRAPH 2007). vol. 26, No. 3.
  • Jing Y. and Baluja S.. (2008) :PageRank for Product Image Search, 17th International IEEE World Wide Web Conference.
  • Snavely N., Seitz S.M., Szeliski R. (2006). Photo tourism: exploring photo collections in 3D, ACM SIGGRAPH, pp 835-846.
  • Stone, Z.; Zickler, T.; Darrell, T., Toward Large-Scale Face Recognition Using Social Network Context, Proceedings of the IEEE , vol.98, no.8, pp.1408,1415, Aug. 2010

Assessment methods

  • Immanent assessment
  • Report & Life Demos
  • Rading and understanding a scientific paper
Machine Learning 2 (MAL2)
German / ILV
3.00
2.00

Course description

This course gives an introduction to artificial neural networks with many layers ("deep learning"). Using the open-source framework TensorFlow from Google (used in both research and industrial settings), we teach concrete skills for typical applications (image understanding, natural language processing).

Methodology

Lectures, self studying, programming assignments, group project

Learning outcomes

After passing this course successfully students are able to ...

  • Model machine learning tasks with deep neural networks
  • Use appropriate data for automatic training of such networks
  • Assess the training progress and adjust the training parameters accordingly

Course contents

  • Introduction to and basics of neural networks (deep learning)
  • Deep learning with TensorFlow and TensorBoard
  • Neural architectures for various tasks (text, audio, images, ...)

Prerequisites

Programming skills (we use Python) Basic knowledge of linear algebra (matrix multiplications, etc.) Prior knowledge in machine learning is useful but not strictly required.

Literature

  • References will be on Moodle and given during lectures

Assessment methods

  • Programming assignments
  • Group project
  • Written exam

Anmerkungen

The course material (presentation slides) will be in English.

Mental Power for IT Disciplines (MIT)
German / ILV, FL
3.00
2.00

Course description

In thus course you will learn to use the whole capacity of your brain to solve problems and to achieve any goal you wish.

Methodology

- Seminar - Distant Learning

Learning outcomes

After passing this course successfully students are able to ...

  • formulate goals you want to achieve which are suitable for your subconsious mind
  • practicing basic elements of attention meditation
  • focus the consciousness mind on goals to align unconscious processes

Course contents

  • Processing of information in the human brain
  • Consciousness and unconsciousness parts of the brain
  • Gaining consciousness control of primarily unconsciousness parts of the brain
  • Using skill full meditation techniques to improvebusiness performance

Prerequisites

Completion of all previous MSE courses

Literature

  • James Borg, "Mind Power", Pearson 2010
  • Kazuo Inamori, "A Compass to Fulfillment", Mc Graw Hill 2010
  • Heinz Hilbrecht, "Meditation und Gehirn", Schattauer, 2010
  • Richard Bandler, "Veränderung des subjektiven Erlebens", Jungfern Verlag 2007, Original: "Using your brain - for a change", Real People Press, U.S. (August 1985)
  • Henry P. Stapp, "Mindful Universe" 2nd Edt Springer 2011
  • Chade-Meng Tan "Search Inside Yourself" Optimiere dein Leben durch Achtsamkeit, Goldmann Verlag 2015

Assessment methods

  • Continuous assessment
Mobile Application Engineering (EPMA)
English / ILV, FL
3.00
2.00

Course description

Introduction into app development for Android and iOS.

Learning outcomes

After passing this course successfully students are able to ...

  • After successfully completing the course, students are able to
  • develop Android and iOS Apps, using the latest Development Environment and Toolchain
  • describe the Liefcycle of Smartphone Applications and explain common concepts in the areas of Testing, Publishing, Marketing & Business Models
  • estimate the required resources for a feature implementation on Android and iOS

Course contents

  • Android and iOS app development and source control management with Git.

Prerequisites

Basic software development experience with Java / C/C++ / Objective C.

Literature

  • Joseph Anuzzi Jr, Lauren Dracay, Shane Conder (2014): Advanced Android Application Development, Addison-Wesley Professional Neil Smyth (2015): iOS 8 App Development Essentials - Second Edition: Learn to Develop iOS 8 Apps using Xcode and Swift 1.2, CreateSpace Independent Publishing Platform

Assessment methods

  • Participation, development of the project, delivery dates, clean source code with comments and Git commits.
Selected Topics Software Engineering 2 (AKS2)
English / ILV, FL
3.00
2.00

Course description

Introduction to the Semantic Web and Linked Data

Methodology

Seminar and distand learning

Learning outcomes

After passing this course successfully students are able to ...

  • explain the main concepts related to the semantic web,
  • explain how to publish, share, and query data on the semantic Web.

Course contents

  • Day 1. Units 1 and 2. Introduction to the SW. Motivation. Main definitions. The web as a database. The web of documents vs. the web of data.Day 1. Units 3 and 4. The Semantic Web stack. The RDF data model. Triples, RDF graphs, Data sets. Blank nodes. Data types. Reification. Languages: N3 and Turtle. RDFS: inference basics. Class practice RDF.3. Day Units 5 and 6. The SPARQL query language. Basic Graph patterns (BGP). SPARQL 1.1 syntax. Formas: SELECT, CONSTRUCT, ASK, DESCRIBE. Agregation. FILTER, OPTIONAL clauses. Subqueries. UNION. SPARQL Update, SPARQL Protocol.Day 2. Units 7 and 8. Linked Data principles. Linked Data 5-star. Open Data. data acquisition: Open Refine, R2RML (RDB2RDF). Vocabularies. Endpoints. real-world examples. Publishing statistical data: the QB vocabulary.Distance Work. There are three projects. First, a list of exercises to be solved by the students, about basic SW concepts (6 units). A second project in modeling a database in RDF, and querying it in SPARQL (10 units). The third project is about representing and querying statistical data on the SW (4 units).

Prerequisites

Bachelor level in computer science

Literature

  • 1. Renzo Angles and Claudio Gutierrez. Subqueries in SPARQL. In Pablo Barcel´o and Val Tannen, editors, AMW, volume 749 of CEUR Workshop Proceedings. CEUR- WS.org, 2011.2. Marcelo Arenas and Jorge P´erez. Querying semantic web data with sparql. In Maurizio Lenzerini and Thomas Schwentick, editors, PODS, pages 305–316. ACM,2011.3. Dave Beckett. N-Triples, 2004.4. Dave Beckett and Tim Berners-Lee. Turtle - Terse RDF Triple Language, 2011.5. Tim Berners-Lee. Notation 3, 2006.6. C. Bizer, T. Heath, and T. Berners-Lee. Linked data-the story so far. International Journal on Semantic Web and Information Systems (IJSWIS), 5:1–22, 2009.7. R. Cyganiak. A relational algebra for SPARQL. Digital Media Systems Laboratory, HP Laboratories Bristol, 1:2005–170, 2005.8. S. Das, S.Sundara, and R. Cyganiak. R2RML: RDB to RDF Mapping Language, 2012.9. Peter Hayes and B. McBride. RDF Semantics, 2004.10. Pascal Hitzler, Markus Krotzsch, and Sebastian Rudolph. Foundations of Semantic Web Technologies. Chapman & Hall/CRC, 2009.11. B. Kampgen and A. Harth. No size fits all - running the star schema benchmark with SPARQL and RDF aggregate views. In The Semantic Web: Semantics andBig Data, volume 7882 of LNCS, pages 290–304. Springer, 2013.12. J. P´erez, M. Arenas, and C. Gutierrez. Semantics and Complexity of SPARQL. ACM Transactions on Database Systems (TODS), 34(3):1–45, 2009.13. A. Vaisman and E. Zimanyi. Data Warehouse Systems: Design and Implementation. Springer, 2014.

Assessment methods

  • The final course grade will be the average of the marks of the three projects.

Anmerkungen

The lesson is done in a cooperation with ITBA Buenos Aires

Selected Topics in Software/App Management (AKSM)
English / ILV, FL
3.00
2.00

Course description

Indepth kowledge about the interfaces between management and IT. The viewpoints of companies, as startups and corporates and their managers and CIOs are being approached with a focus on the business aspect.

Learning outcomes

After passing this course successfully students are able to ...

  • After passing this course successfully students are able toDefine Project order, limits, context analysis• Draw Project portfolio-Management and corporate strategies• Define international technology expoitation• explain tools and approaches• Identify international technology expoitation networks

Course contents

  • • 2 VO Project management• 4 VO Project portfolio management• 1 VO Basics Technology exploitation• 4 VO From Prototype to an international high performance product• 1 VO Technology expoitation networks for companies • 2 VO Jury-Pitch• 14 FL Feedback

Prerequisites

Basics Project Management

Literature

  • Chapters from: Blue Ocean Strategy, W. Chan Kim and Renée Mauborgne• „A Guide to the Project Management Body of Knowledge“, Project Management Institute (PMI)Additional German literature:• Kapitel „Technologievermarktung“ aus E-Book Technologiemanagement (siehe Unterlagensheet, Vorbereitung vor der LV empfohlen)• Standard Projekthandbuch der PMA (Projekt Management Austria)• „Projektmanagement: Leitfaden zum Management von Projekten, Projektportfolios und projektorientierten Unternehmen“, Gerold PATZAK und Günter RATTAY• „pm baseline 3.0“, Projekt Management Austria (PMA)

Assessment methods

  • Course immanent assessment method and group assignment

Anmerkungen

Hands-on course: Experts from corporates/startups are being invited/visited

Social Platforms (SPLF)
German / ILV, FL
3.00
2.00
Module 4.2 Master Thesis (MOD42)
German / iMod
24.00
-
Master's Thesis (MT)
German / SO
21.00
0.00

Course description

In the course each student develops a technical and practically oriented master’s thesis on a scientific level

Methodology

selfdirected learning

Learning outcomes

After passing this course successfully students are able to ...

  • After passing this course successfully students are able to
  • draft a master’s thesis on a scientific level
  • acquire knowledge in the field of the master’s thesis in self-study
  • answer a research question in the field of software engineering
  • explain the bigger picture
  • assess the significance and weight of influential factors, data, and other relevant information
  • present the relevant state of technology and company environment
  • analyze and present the larger technical and socio-economic context

Course contents

  • Independent scientific work of students under the guidance of the supervisor

Prerequisites

Completion of all previous courses of the study program

Literature

  • relvant references for the topic of the master´s thesis

Assessment methods

  • Assessment of the master’s thesis by first and second advisor

Anmerkungen

The course is not displayed in the timetable and no attendance records are kept.

Thesis Seminar (SMT)
German / SE
3.00
2.00

Course description

In the course relevant findings are discussed between the supervisor and the student, and the progress of the master’s thesis is evaluated.

Learning outcomes

After passing this course successfully students are able to ...

  • acquire knowledge in the field of the master’s thesis in self-study
  • present findings of the master’s thesis and explain technical relationships in the field of the master’s thesis
  • develop and answer a research question in the field of software engineering
  • analyze the significance and weight of influential factors, data, and other relevant information
  • analyze and present the larger technical and socio-economic context

Course contents

  • Presentation of the findings of the master’s thesis
  • Explanation of the technical relationships in the field of the master’s thesis

Prerequisites

Completion of all previous courses of the study program

Literature

  • In Abhängigkeit von der Forschungsfrage und dem Thema der Masterthesis relevante Fachbücher, Publikationen, White Papers, Studien, …

Assessment methods

  • Assessment of master’s thesis by first and second advisor

Anmerkungen

The supervision of the master’s thesis is done on an individual basis in synchronous or asynchronous settings and is supported by modern communication tools. The course is not displayed in the timetable and no attendance records are kept.